To connect the abstract definition of a query and explanation to concrete IML methods, we outline the state-of-the-art in the field and the direction it is progressing. We ignore methods used for sampling data or estimating models since they are beyond the scope of this work. The selection of the IML method fulfilling the query \(Q\)  is one of the most crucial steps in the process. We structure the query-class \(\mathcal {Q}\)  into interpretable models and explanatory methods. Interpretable models are of cause deeply connected to the selection of the model-class \(\mathcal {M}\)  but describe the inherit interpretable mechanism of the model, detached from other architectural designs. The most prominent class of interpretable models are classical statistical models such as generalized, mixed, additive, or simpler linear models [1]}, [2]}. Neural additive models [3]} are an extension of additive models, making them capable to be applied to more complex problems where they otherwise lack predictive performance. Other machine learning-based interpretable models include interpretable representations (disentangled or factorized representations) [4]}, [5]}, [6]}, sparse (input) models [7]}, [8]}, or graph models (causal graphs or various tree-based methods) [9]}, [10]}, [11]}, [12]}. Proxy interpretable models, which do not have the main objective to be interpretable but still have an interpretable component, include, e.g., attention-based [13]} and probabilistic models [14]}. Probabilistic models allow for interpretative statements about generative processes and uncertainties.
<FIGURE>